The goal of multi-agent systems is to build robust intelligent systems capable of existing in complex environments. Agents must decide with whom to interact. In this paper we investigate how agents may bias their interactions in environments where alternative game payoffs are available. We present a number of game theoretic simulations involving a range of agent interaction models. Through a series of experiments we show the effects of modelling agent interactions when games representing alternative levels of benefit and risk are offered. Individual agents may have a preference for games of a certain risk. We also present analysis of population dynamics, examining how agents bias their peer interactions throughout each generation. We also address the topic of implicit trust, where agents reflect levels of trust through the payoffs presented in a game offer. In this interaction model agents may use levels of trust to choose opponents and to determine levels of risk associated with a game.